Integrating Image Analysis and Dental Radiography for Periodontal and Peri-Implant Diagnosis
Bibliographic record
Abstract
The recent change in classification of periodontal and peri-implant diseases includes objective evaluation of intra-oral radiographs and quantification of bone loss for disease staging and grading. Assessment of the progression of periodontal disease requires deduction of bone loss longitudinally, and its interpretation as (1) a percentage in relation to tooth root and (2) as a function of the patient's age. Similarly, bone loss around dental implants, after accounting for initial remodeling, is central for determining diagnosis, severity, and progression of peri-implantitis. Bone gain secondary to periodontal regeneration can be measured using standardized dental radiographs and compared to baseline morphology to determine treatment success. Computational image analysis, including machine learning (ML), has the potential to develop and automate quantitative measures of tooth, implant, bone volumes, and predict disease progression. The developed algorithms need to be standardized while considering pre-analytic, analytic, and post-analytic factors for successful translation to clinic. This review will introduce image analysis and machine learning in the context of dental radiography, and expand on the potential for integration of image analysis for assisted diagnosis of periodontitis and peri-implantitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".